Knowledge-Based Support for Scholarly Editing and Text Processing

Kittelmann, Jana
MLU Halle-Wittenberg, Deutschland

Wernhard, Christoph
TU Dresden, Deutschland


1. Introduction

1.1. Background: Large Knowledge Bases

A large portion of the material on which scholarly editing is based today is available electronically in large knowledge bases. Some of these emerge from the archive, library and museum communities, for example Kalliope. Such efforts require the use of standardized vocabularies and databases of entities such as persons and locations. Kalliope thus links to Gemeinsame Normdatei (GND), which provides more than 120 million facts about approximately 11 million entities. The prevailing technique to realize such linked knowledge bases is the Semantic Web, as advocated by the W3C, characterized by the use of ontologies to express standardized vocabularies, global identifiers (URIs) and the possibility to express knowledge in a machine understandable way as subject-predicate-object statements with RDF. Further large knowledge bases, such as Yago (Hoffart et al. 2013) and DBpedia (Lehmann et al. 2015), developed mainly in computer science with Semantic Web techniques, gather and combine machine processable knowledge from "crowd-maintained" sources like Wikipedia and centrally maintained sources like GND or GeoNames.

1.2. Beyond TEI

The seemingly best developed machine support for scholarly editing today is provided with the Text Encoding Initiative (TEI) format, based on document markup. URIs as attribute values of markup elements can provide links to knowledge bases. Envisaged applications include in particular the rendering for different media and extraction of metadata. Some of the recent developments are actually orthogonal to the OCHCO text model and its representation through XML, core characteristics of the original TEI. Connecting TEI with Semantic Web techniques, data modeling and ontologies is, for example, an ongoing topic of discussion (e.g. Eide 2015). Recent versions of TEI provide support for names, dates, people, and places as well as linking, segmentation, and alignment (The TEI Consortium 2015: Chapters 13 and 16). In a broad long-term perspective, important aspects that further go into these directions become apparent:

Addressing these issues, we approach the requirements of today's scholarly editing here from the view of computational logic: What can logics – as machine processable symbolic languages with formally specified semantics – contribute? A starting point is that with Semantic Web technology the large knowledge bases can already be considered as large sets of logic facts. Logic languages have various further potential roles in machine supported scholarly editing, such as specifying properties and values associated with texts, specifying pieces of text, specifying knowledge sources and their combination, and specifying inferences involved in automated computation of information associated with texts.

2. Knowledge-Based Support for Scholarly Editing

2.1. High-Quality Support at all Phases

Three main phases of machine assisted scholarly editing can be identified, which all should be supported: (1) Creating the enhanced object text; (2) Generating intermediate representations for inspection by humans or machines; (3) Generating consumable presentations. Support for all three phases should be of high quality – for example entity recognition should precisely identify persons, or the print layout of a finally rendered document should be professional.

2.2. Issues of Integrating Different Types of Knowledge

High-quality support is not possible without inclusion of specialized techniques and the combination of automated techniques with information and adjustments provided by humans. The adequate support of this combination is an important aspect where the considered scenario differs from conventional programming or query languages. Relevant techniques include non-monotonic reasoning, semantics-based knowledge partitioning (Wernhard 2004, Ghilardi et al. 2006, Cuenca Grau et al. 2008, Kontchakov et al. 2010) and the use of explanations for inferred information, as exemplified by proofs in mathematical knowledge bases (Urban et al. 2013). A further important integration requirement concerns the combination of statistics-based techniques, which are essential for natural language processing operations such as named entity recognition or keyphrase extraction, with a symbolic logic-based framework.

2.3. External Annotations

The availability of powerful techniques to identify places in text – based on syntactic as well as semantic properties – suggests to prefer external annotations to in-place markup. Annotations are then maintained separated from the object text in annotation documents. An automated processor creates an annotated document by merging annotations and object text.

2.4. Representation of Epistemic Status

Scholarly editing requires to associate various forms of epistemic status with facts, which is interesting to model formally from the viewpoint of artificial intelligence. Consider for example a creation date associated with written communication: it can be given by its author or can be inferred – by the editor or by a machine, it can be only partially specified by the author, it can be specified with different precision, considered as a point or range in time, etc. The current version of TEI offers some related elements to indicate certainty, precision and responsibility (The TEI Consortium 2015: Chapter 21), but these are not based on any formal semantic treatment and it is seems hardly possible to express the sketched date examples with them.

2.5. Utilizing Inferred Access Patterns

Efficient access to large knowledge bases requires caching and preprocessing, which ideally should be performed automatically on the basis of the queries performed by the knowledge processing engine. Relevant techniques come from optimization in databases (Toman / Weddell 2011) and in first-order model computation systems (Pelzer / Wernhard 2007). It seems that recent techniques for view-based query processing (Calvanese et al. 2007) based on variants of Craig's interpolation and second-order quantifier elimination (Toman / Weddell 2011; Bárány et al. 2013; Wernhard 2014) where access patterns can be specifically considered in an abstract way (Bárány et al. 2013) are particularly useful. Logic-based languages for programming as well as data access facilitate the application of such abstract techniques. For an overview on alternate ways to associate computational meaning with logics see (Kowalski 2014).

2.6. The Role of Ontologies

Ontologies are an important ingredient for the Semantic Web because they provide agreed vocabularies. However, to evaluate queries arising in the text processing tasks of scholarly editing, ontology reasoning alone is not sufficient. Also, the basic ontologies relevant in the context of scholarly editing are – in contrast to the biomedical area (Horrocks 2013) – rather small and trivial.

3. A Prototype: The KBSET System

Important issues of complex computer systems often become apparent only with applications. Thus, the authors developed the KBSET system, an experimental platform to clarify the precise requirements of machine support for scholarly editing and to experiment with advanced techniques. It follows the outlined approach, but, so far, only realizes some of the discussed aspects. A draft version of an edition of Max Stirner: Geschichte der Reaction, Band 1. Berlin, 1852 accompanies it as comprehensive example. The system is free software and available from

In a typical setting, the system takes as inputs:

  1. A source text file, possibly in LaTeX format. The system can parse LaTeX, where the set of recognized commands is configurable, including user defined commands as well as commands that establish some "ordered hierarchy of content objects". In this way plain or structured text is available within the system to modules that operate on such text models.
  2. Annotation documents, that is, text files with annotations, possibly in LaTeX format. The associated places in the source text to which they are referring are specified abstractly.
  3. Large fact bases, currently in particular GND and GeoNames, as well as extracts from YAGO2 and DBpedia.
  4. A so-called assistance document, that is, a configuration file, where, among other things, the fact bases are specified and information is given to bias or override automated inferencing such that fully correct results are obtained.

A user interface is provided that integrates the system into the Emacs editor, which is free software. The system includes a facility for named entity recognition, which – essentially based on GND and GeoNames as gazetteers – identifies persons, locations and dates. The system produces a variety of outputs, supporting all the phases of scholarly editing mentioned above:

A typical application would be the development of an annotated essay or book, where the source text is edited in LaTeX and the configuration evolves step-by-step until the inferred information is fully correct.

4. Acknowledgments

This work was supported by Alexander von Humboldt-Professur für neuzeitliche Schriftkultur und europäischen Wissenstransfer and by DFG grant WE 5641/1-1 .

Appendix A

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